What is GEO (Generative Engine Optimization)?

Generative Engine Optimization, or GEO for short, is the answer to the change we're all feeling. search engines cease to be mere directories of links and become a place where the user immediately receives a ready answer generated by the language model. GEO thus means not only adapting content to Google's algorithms, but also to systems like LLM (Large Language Models), which interpret questions, combine information and present it in the form of a short summary.

You could say it's the natural evolution of SEO. Until now, we have been writing with page indexing robots in mind. Now we have to additionally take into account how the artificial intelligence will read the meaning of our text and whether it will interpret it as valuable material to present to the user. 

Differences between GEO and classic SEO

• SEO optimizes for search engine robots, GEO for language models
• SEO is a game for position in the results list, GEO is a fight for presence in the answer
• In SEO it's the technical structure that counts, in GEO it's the sense, consistency and completeness of the statement
• GEO content must clearly answer specific user questions
• Understated or vague texts are simply overlooked by LLM

The role of artificial intelligence (AI) in LLM positioning

Just a few years ago, the talk was about search engine algorithms; today the conversation is about language models. AI has become a filter through which the user gets answers. It doesn't just evaluate the words that weave into the text. It analyzes the meaning of the entire paragraph, the tone of speech, and even the context in which the information appears. 

LLMs as new "search engines"

Language models such as GPT or BERT are increasingly acting as guides to the Web. Instead of a list of links, they provide an answer that can be used right away. 

For marketers, this means a huge change. The text, which previously fought for a place in the TOP 10, now competes to be quoted in the response generated by the model. It's a bit of a different race - it's not just the visibility of the page that counts, but also whether the content is complete and logical enough for the system to consider it the best answer.

Key GEO strategies for marketers

Generative Engine Optimization requires a different approach to content creation than classic SEO. Here, it's not just a matter of technically matching the text to the search engine, but making sure that the responses generated by the language models benefit precisely from our content. That's why Marketers need to think of articles, descriptions or guides in terms of valuable sources, which AI will find useful and worthy of quoting.

Create content tailored to AI-generated responses

LLM models do not select passages at random. They analyze all the material and only then decide whether it can be used in a response. The content should therefore answer specific user questions, preferably in the form of short, clear paragraphs. Sentences that sound like ready-made explanations on their own work well.

However, this does not mean that the text has to be dry and textbook. On the contrary, a natural, friendly style increases the chance that a user will stay on the site, a signal that also matters in LLM positioning.

The importance of context and user intent

Generative Engine Optimization is not just a play on phrases, but more importantly, the ability to predict what is really behind a query typed into a search engine. A user asks "how to improve a site's visibility in LLM," but in practice he expects practical guidance, not definitions. If the text accurately reads this intention, AI has a reason to cite it.

That's why it makes sense to write from the perspective of someone who understands the problem and suggests a step-by-step solution. GEO rewards content that is grounded in real needs, not just technically correct.

Use of data and analytics in GEO

By analyzing users' questions, studying search trends and tracking which content is actually cited in AI responses, you can constantly improve your GEO strategy. It's more precise work than in classic SEO, because it's not so much about ranking position as it is about presence in the generated response.

Marketers should therefore look not only at organic traffic, but also at whether content appears in the context of user questions. This perspective allows them to build an advantage where competitors have yet to adapt.

GEO in practice - how to write content that actually gets models to respond?

Here are some proven ways to increase the chances that just your text will be quoted.

1. FAQs and short answers to questions

There is no better place to "catch" the attention of language models than a well-written FAQ. Simple structure, specific question, clear answer, which is exactly what LLM systems are looking for. 

Why create FAQs with GEO in mind?

• Helps answer specific questions from customers before they even ask them
• Each answer can be quoted by LLM as an expert excerpt
• Creates a structural AI signal - models better understand what the content refers to
• FAQ section grows with your offerings, market needs and search trends

What questions are worth posting?

1. informational (factual)

• How much does [service] cost in 2025?
• What does [service name] include?
• How long does the project take to complete?

2. comparative and decision-making

• [Option A] or [Option B] - which to choose?
• Is it worth implementing [the solution] now?
• How do I choose a company for [service name]?

3. image (recommended/best)

• The best marketing agency in Krakow?
• Recommended company for [service] in 2025?
• Who does [the service] best?

2. format /answers/

A good supplement to FAQs are mini-pages in the format /answers/[question]/. Each sub-page should include:

• H1 in the form of a question,
• A brief TL;DR-style introduction,
• bulleted response,
• sources or links to elaborate,
• A natural mention of the brand.

Such an arrangement gives AI models a clear signal that the content is a ready answer and suitable for citation in the generated results.

3. tables that organize information

Models love tables, because the information in them is organized and unambiguous. You don't have to wonder what to compare with what - you can see everything in black and white.

• The table makes it easy for the model to "draw" ready comparisons.
• The reader makes a decision faster because he doesn't have to arrange the information in his head on his own.
• It also signals that your site is a source of structured knowledge, and that's exactly the kind of content models are looking for.

4. unique data that only you have

Models generate answers based on content available on the Internet. If your company shows something that no one else has, such as average turnaround time, numbers of inquiries or lessons learned from your own practice - you have an advantage. 

• Unique data sets your site apart from repetitive content.
• They give the model an "excuse" to just quote you, because you are the only source.
• The reader gets more than generalities - real numbers and facts.

5. opinions and mini case studies

Models are getting better at capturing feedback and real-life examples. If you show a short customer story: problem, solution, result, you create content that can be easily quoted in response. Why it works.

• Reviews and cases are proof of credibility, not just a statement.
• This is content that fits well with "what works in practice" style answers.
• By showing the process step by step, you make the model and the reader can better understand your service.

6. content labeling (schema.org)

Schema.org is a project jointly created by Google, Microsoft and Yahoo that acts as a dictionary for search engines and language models. It allows you to "hint" to machines what exactly is on a page.

Normally, the bot sees only text and has to guess whether it's a service description, an opinion or a question in the FAQ. Schema.org tags add a label: "this is an FAQ," "this is a customer review," "this is a step-by-step guide."

Why is this important?

• Facilitates interpretation of content: models do not have to guess, they know immediately what is what.
• Increases chances of being cited: A well-marked FAQ or opinion is much easier for AI to cite in response.
• Doesn't change the look of the site for people: the user sees exactly the same thing, but the background content is more "friendly" to the systems.
• Gives competitive advantage: Many companies are still not implementing this, and it's an easy way to stand out in the results and in the responses generated.

Example: On the service page, you can mark the price list as Service, opinion section as Review, and the most frequently asked questions as FAQPage. A search engine or model, seeing these labels, treats your site as a well-structured knowledge base.

Extended structural data

Schema.org also allows you to tag elements that build brand credibility. It is not only about services or opinions, but also organizational data:

• Organization - Company name, address, logo, contact information, links to social profiles.
• Service - A description of each service, with scope and conditions.
• Author - designation of content authors with a short bio and a link to the expert's profile.
• LocalBusiness - Detailed local information, such as opening hours, location or phone number.

With such designations, LLM models get a consistent picture of who you are and what you offer. 

Structured data is the foundation, but tags alone are not enough. Google and LLM models are increasingly looking at who is behind the content and whether they can be trusted. That's why it makes sense to implement E-E-A-T in a real way:

• Author's signature next to each article with a short bio, a link to LinkedIn and a photo.
• Data sources - Links to studies, industry reports and technical documentation.
• Mini case study - Short examples of projects that demonstrate the company's experience.
• Reviews and feedback embedded on the page in structured form.

Such elements make the content credible not only to search engines, but also to readers, who will more quickly recognize the brand as trustworthy.

7. llms.txt file as a signal for AI

The llms.txt file acts as a map for language models. It's a simple, textual manual hosted on the server where a brand can specify the most important information about itself and make it easier for AI to quote content correctly. 

What can be included in llms.txt?

• company data (name, address, contact)
• content authors and their competencies
• description of services and products
• citation policy and preferred reference sources
• short FAQ with questions and answers

Such a file gives brands control over how AI models interpret their site and what snippets can go into the generated responses.

However, it is worth adding a caveat: Google doesn't officially use llms.txt and experts today compare it to the old "meta keywords" - an interesting idea, but virtually useless for now. So treat this file more as an experiment or an internal exercise to organize brand information, rather than a real visibility factor.

8. deep search for your company in ChatGPT

This is a practice that is slowly becoming a standard in GEO. It involves asking questions about your company, services and collaboration process in ChatGPT from time to time (e.g., once a month). Sound trivial? Yet this way you can see how the model actually describes your business and what you should improve on the site. In addition, the more often such queries occur, the more often AI models interact with your content, which translates later into including this information in the training data of new releases. 

A sample question you can ask periodically:

"Prepare a detailed analysis of the company [COMPANY NAME] in the context of [SERVICE, e.g., website development, oncology consultation, etc.]. Check all available information about the offerings, the process of working with clients, the potential advantages and disadvantages of using their services, and how the company is described online. Also include customer reviews, if available, and information about additional services they offer outside of their core business. Finally, summarize whether [COMPANY NAME] can be seen as a reliable and complete partner to [SERVICE], and what elements of their communications or offerings would be worth refining to enhance visibility in the results generated by the language models."

9. AI signals - a new dimension of visibility

"AI signals" is a concept that is increasingly coming up in discussions about GEO. It refers to brand visibility in responses generated by language models (ChatGPT, Claude, Perplexity, Gemini). Such mentions are becoming a new channel for online presence and a signal that content is valuable in the AI ecosystem.

What could AI signals be?

• citing the domain in the response (e.g., active link),  
• A paraphrase of the content from your site,  
• Recalling the brand name in the description,  
• A recommendation of your product or service.  

Why is this important?

• Google has not announced that it treats AI signals as a ranking factor, but visibility in AI Overviews or in ChatGPT responses realistically generates traffic,  
• AI citations usually coincide with content well optimized for SEO + E-E-A-T,  
• The SEO industry is treating AI signals as the new equivalent of backlinks or snippets.  

How to monitor AI signals?

• do the aforementioned deep search of your brand and its services in ChatGPT, Claude, Perplexity,  
• use tools like Chatbeat or Perplexity Analytics that track domain citations,  
• Analyze which competitor content AI cites more often - and improve yours (e.g., FAQs, data, case studies).  

AI signals are not a new Google ranking, but evidence that your brand is gaining "AI visibility." It's worth monitoring and developing, because it's a real influence on brand awareness in a world where artificial intelligence is increasingly providing the answers.

How to measure the effects of GEO?

Optimization itself is half the job. The other half is checking whether it actually delivers results. How to measure this in practice?

Citation and visibility monitoring tools

Solutions are already emerging that track how your content is used in AI responses. An example is chatbeat.com, which shows if and where models are calling up your site. 

Chatbeat works by monitoring the responses generated by popular language models (e.g. ChatGPT, Perplexity, Gemini, Claude or other models) and seeing if there are mentions of your brand or content from your site. This allows you to see the indicative presence of your expertise in user conversations with AI.

Content quality analysis in practice

It's worth checking regularly to see which parts of the page appear in the responses. If models are eager to quote FAQs or tables, it's a sign that this format is working and worth developing. If, on the other hand, they omit opinions or descriptions of the process, perhaps they are too general and need to be made more specific.

Comparison tests

Ask models the same questions from time to time and record the answers. You'll see how their content changes after updates to the site. It's an easy way to track whether the new materials are actually increasing visibility.

Feedback from users

You don't always have to rely solely on tools. Ask your customers where they came to the site from or if they have used AI chatbots and assistants. This is an additional source of information that can be helpful. `

Challenges and Opportunities of Generative Engine Optimization

Any change in the way search engines operate raises concerns, but also offers great opportunities. GEO is no exception. The biggest challenge becomes predicting how language models interpret content and what makes them select some sources and omit others. This is still a largely experimental area, so it is difficult to talk about fixed rules.

On the other hand, it is in this uncertainty that opportunity lies. Brands that are the first to learn how to write content aligned with LLM positioning can gain a huge advantage. Visibility in AI-generated responses is becoming something as important as first place in Google results used to be.

Summary - why should you think about LLM positioning?

The world of search is changing faster than many marketers would like to admit. Just yesterday we were fighting for the first page of Google, today what's at stake is a presence in the responses generated by language models. These are beginning to determine which brands will gain visibility and which will remain in the shadows.

LLM positioning is not a fad, but a new direction for the entire industry. If the brand does not adapt to this trend, it will quickly lose ground.

That's why it's a good idea to start thinking about GEO now. It's not about a revolution in a day, but about making changes gradually: from the style of writing, to the structure of the content, to the way results are analyzed. It's a process in which those who dare to act earlier will gain the advantage.

Does this mean that classic SEO is no longer important? Absolutely not. However, without SEO in LLM, the entire online visibility strategy will be incomplete. And since the future of search is happening here and now, it's better to be a part of it than just an observer.

If you want to strengthen your brand's presence in AI-generated responses and leverage the full potential of GEO, contact us - We will help you do it professionally.

Sources: 

1. Search Engine Land, What is generative engine optimization (GEO)?, available online: https://searchengineland.com/what-is-generative-engine-optimization-geo-444418
2. Schema.org, "Service - Schema.org Type," available: https://schema.org/Service
3. Google Search Central, SEO Starter Guide: The Basics, available: https://developers.google.com/search/docs/fundamentals/seo-starter-guide